{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates data slicing for the National Renewable Energy Laboratory (NREL) National Solar Radiation Database (NSRDB) data. The data is provided from Amazon Web Services using the HDF Group's Highly Scalable Data Service (HSDS). These slicing methods would also work with the WIND Toolkit data available via HSDS at /nrel/wtk/\n",
    "\n",
    "Please consult the README file for setup instructions prior to running this notebook.\n",
    "\n",
    "This example will also be using NREL's Renewable Energy Potential (V) model [`NREL-reV`](https://github.com/nrel/reV) which can be installed with pip:\n",
    "```\n",
    "pip install NREL-reV\n",
    "```\n",
    "or with conda:\n",
    "```\n",
    "conda install nrel-rev\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'rev.generation'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minline\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      9\u001b[0m sns\u001b[38;5;241m.\u001b[39mset_style(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhite\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrev\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneration\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneration\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Gen\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrex\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Resource\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrex\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutilities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mloggers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m init_logger\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rev.generation'"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import time\n",
    "%matplotlib inline\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "from rev.generation.generation import Gen\n",
    "from rex import Resource\n",
    "from rex.utilities.loggers import init_logger\n",
    "\n",
    "cwd = os.getcwd()\n",
    "bin_dir = os.path.join(os.path.dirname(cwd), 'bin')\n",
    "\n",
    "init_logger('reV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rev==0.1.1\n"
     ]
    }
   ],
   "source": [
    "! pip freeze | grep -i rev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solar Generation\n",
    "Extract meta data and find site \"gids\" associated with Boulder, Colorado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsrdb_file = '/nrel/nsrdb/v3/nsrdb_2013.h5'\n",
    "with Resource(nsrdb_file, hsds=True) as f:\n",
    "    nsrdb_meta = f.meta\n",
    "    nsrdb_ti = f.time_index\n",
    "    print(f.get_dset_properties('dhi'))\n",
    "    \n",
    "nsrdb_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state = 'Colorado'\n",
    "nsrdb_points = nsrdb_meta['state'] == state\n",
    "nsrdb_points = nsrdb_meta.index[nsrdb_points].tolist()\n",
    "nsrdb_points_meta = nsrdb_meta.loc[nsrdb_points[::10]]\n",
    "print('Number of NSRDB points in {}: {}'.format(state, len(nsrdb_points)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run reV Gen for every 10th site in Colorado, USA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SAM configuration for wind turbine\n",
    "config_path = os.path.join(bin_dir, 'pv_SAM_config.json')\n",
    "with open(config_path, 'r') as f:\n",
    "    SAM_config = json.load(f)\n",
    "    \n",
    "SAM_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "# Run reV Gen in parallel\n",
    "ts = time.time()\n",
    "pv_gen = Gen.reV_run(tech='pvwattsv5', points=nsrdb_points[::10], \n",
    "                     sites_per_worker=5,sam_files=config_path,\n",
    "                     res_file=nsrdb_file, fout=None, max_workers=os.cpu_count(),\n",
    "                     output_request=('cf_mean', 'cf_profile'))\n",
    "\n",
    "tt = time.time() - ts\n",
    "print('Time to compute generation for {} sites = {:.4f} min'\n",
    "      .format(len(nsrdb_points[::10]), tt / 60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mrossol/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/seaborn/utils.py:279: UserWarning: Use the colorbar set_ticks() method instead.\n",
      "  ax_i.set_yticks(newticks)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract CF means\n",
    "cf_means = nsrdb_points_meta[['latitude', 'longitude']].copy()\n",
    "cf_means['cf_means'] = pv_gen.out['cf_mean'] / 1000\n",
    "\n",
    "cf_means.plot.scatter(x='longitude', y='latitude', c='cf_means', cmap='Purples')\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract CF profiles\n",
    "cf_profiles = pd.DataFrame(pv_gen.out['cf_profile'] / 1000, columns=points)\n",
    "cf_profiles['DateTime'] = nsrdb_ti\n",
    "cf_profiles['Month'] = nsrdb_ti.month\n",
    "cf_profiles['Hour'] = nsrdb_ti.hour\n",
    "\n",
    "sns_profiles = pd.melt(cf_profiles, id_vars=['DateTime', 'Month', 'Hour'],\n",
    "                       value_name='CF')\n",
    "sns_profiles.index = pd.to_datetime(sns_profiles['DateTime'])\n",
    "\n",
    "mask = sns_profiles.index < pd.to_datetime('2013-01-02')\n",
    "ax = sns.lineplot(x=\"DateTime\", y=\"CF\",\n",
    "             markers=True, dashes=False, data=sns_profiles.loc[mask])\n",
    "plt.title('CF Profile for 2013-01-01')\n",
    "ax.set(ylim=(0, 1))\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "ax = sns.boxplot(x=\"Month\", y=\"CF\", data=sns_profiles)\n",
    "plt.title('Diurnal Distribution')\n",
    "\n",
    "ax.set(ylim=(0, 1))\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "ax = sns.boxplot(x=\"Hour\", y=\"CF\", data=sns_profiles)\n",
    "plt.title('Diurnal Distribution')\n",
    "ax.set(ylim=(0, 1))\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wind Generation\n",
    "Extract meta data and find site \"gids\" associated with Dist. Tehuantepec, Oaxaca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>timezone</th>\n",
       "      <th>elevation</th>\n",
       "      <th>offshore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.603382</td>\n",
       "      <td>-127.617050</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.620419</td>\n",
       "      <td>-127.626007</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.637451</td>\n",
       "      <td>-127.634979</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.654484</td>\n",
       "      <td>-127.643951</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.671509</td>\n",
       "      <td>-127.652924</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    latitude   longitude country state county  timezone  elevation  offshore\n",
       "0  37.603382 -127.617050    None  None   None        -9          0         1\n",
       "1  37.620419 -127.626007    None  None   None        -9          0         1\n",
       "2  37.637451 -127.634979    None  None   None        -9          0         1\n",
       "3  37.654484 -127.643951    None  None   None        -9          0         1\n",
       "4  37.671509 -127.652924    None  None   None        -9          0         1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind_file = '/nrel/wtk/conus/wtk_conus_2013.h5'\n",
    "with Resource(wind_file, hsds=True) as f:\n",
    "    wind_meta = f.meta\n",
    "    wind_ti = f.time_index\n",
    "    print(f.get_dset_properties('dhi'))\n",
    "    \n",
    "wind_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of WTK points in Colorado: 63678\n"
     ]
    }
   ],
   "source": [
    "state = 'Colorado'\n",
    "wind_points = wind_meta['state'] == state\n",
    "wind_points = wind_meta.index[wind_points].tolist()\n",
    "wind_points_meta = wind_meta.loc[wind_points[::10]]\n",
    "print('Number of wind points in {}: {}'.format(state, len(wind_points)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run reV Gen for every 100th point in Colorado, USA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adjust:constant': 0,\n",
       " 'system_capacity': 1620,\n",
       " 'wind_farm_losses_percent': 16.7,\n",
       " 'wind_farm_wake_model': 0,\n",
       " 'wind_farm_xCoordinates': [0],\n",
       " 'wind_farm_yCoordinates': [0],\n",
       " 'wind_resource_model_choice': 0,\n",
       " 'wind_resource_shear': 0.140000001,\n",
       " 'wind_resource_turbulence_coeff': 0.1,\n",
       " 'wind_turbine_hub_ht': 80,\n",
       " 'wind_turbine_powercurve_powerout': [0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  1,\n",
       "  9,\n",
       "  16,\n",
       "  49,\n",
       "  81,\n",
       "  122,\n",
       "  163,\n",
       "  211,\n",
       "  259,\n",
       "  319,\n",
       "  378,\n",
       "  441,\n",
       "  504,\n",
       "  574,\n",
       "  643,\n",
       "  726,\n",
       "  808,\n",
       "  896,\n",
       "  984,\n",
       "  1072,\n",
       "  1159,\n",
       "  1236,\n",
       "  1312,\n",
       "  1369,\n",
       "  1426,\n",
       "  1473,\n",
       "  1519,\n",
       "  1545,\n",
       "  1571,\n",
       "  1583,\n",
       "  1594,\n",
       "  1602,\n",
       "  1609,\n",
       "  1614,\n",
       "  1619,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  1620,\n",
       "  0],\n",
       " 'wind_turbine_powercurve_windspeeds': [0,\n",
       "  0.25,\n",
       "  0.5,\n",
       "  0.75,\n",
       "  1,\n",
       "  1.25,\n",
       "  1.5,\n",
       "  1.75,\n",
       "  2,\n",
       "  2.25,\n",
       "  2.5,\n",
       "  2.75,\n",
       "  3,\n",
       "  3.25,\n",
       "  3.5,\n",
       "  3.75,\n",
       "  4,\n",
       "  4.25,\n",
       "  4.5,\n",
       "  4.75,\n",
       "  5,\n",
       "  5.25,\n",
       "  5.5,\n",
       "  5.75,\n",
       "  6,\n",
       "  6.25,\n",
       "  6.5,\n",
       "  6.75,\n",
       "  7,\n",
       "  7.25,\n",
       "  7.5,\n",
       "  7.75,\n",
       "  8,\n",
       "  8.25,\n",
       "  8.5,\n",
       "  8.75,\n",
       "  9,\n",
       "  9.25,\n",
       "  9.5,\n",
       "  9.75,\n",
       "  10,\n",
       "  10.25,\n",
       "  10.5,\n",
       "  10.75,\n",
       "  11,\n",
       "  11.25,\n",
       "  11.5,\n",
       "  11.75,\n",
       "  12,\n",
       "  12.25,\n",
       "  12.5,\n",
       "  12.75,\n",
       "  13,\n",
       "  13.25,\n",
       "  13.5,\n",
       "  13.75,\n",
       "  14,\n",
       "  14.25,\n",
       "  14.5,\n",
       "  14.75,\n",
       "  15,\n",
       "  15.25,\n",
       "  15.5,\n",
       "  15.75,\n",
       "  16,\n",
       "  16.25,\n",
       "  16.5,\n",
       "  16.75,\n",
       "  17,\n",
       "  17.25,\n",
       "  17.5,\n",
       "  17.75,\n",
       "  18,\n",
       "  18.25,\n",
       "  18.5,\n",
       "  18.75,\n",
       "  19,\n",
       "  19.25,\n",
       "  19.5,\n",
       "  19.75,\n",
       "  20,\n",
       "  20.25,\n",
       "  20.5,\n",
       "  20.75,\n",
       "  21,\n",
       "  21.25,\n",
       "  21.5,\n",
       "  21.75,\n",
       "  22,\n",
       "  22.25,\n",
       "  22.5,\n",
       "  22.75,\n",
       "  23,\n",
       "  23.25,\n",
       "  23.5,\n",
       "  23.75,\n",
       "  24,\n",
       "  24.25,\n",
       "  24.5,\n",
       "  24.75,\n",
       "  25,\n",
       "  25.25],\n",
       " 'wind_turbine_rotor_diameter': 77}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SAM configuration for wind turbine\n",
    "config_path = os.path.join(bin_dir, 'wind_SAM_config.json')\n",
    "with open(config_path, 'r') as f:\n",
    "    SAM_config = json.load(f)\n",
    "    \n",
    "SAM_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-0a2ed400d358>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m gen = Gen.reV_run(tech='windpower', points=points[::100], sam_files=config_path,\n\u001b[1;32m      4\u001b[0m                   \u001b[0mres_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwind_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m                   output_request=('cf_mean', 'cf_profile'))\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/reV/reV/generation/generation.py\u001b[0m in \u001b[0;36mreV_run\u001b[0;34m(cls, tech, points, sam_files, res_file, output_request, curtailment, downscale, max_workers, sites_per_worker, pool_size, timeout, points_range, fout, dirout, mem_util_lim, scale_outputs)\u001b[0m\n\u001b[1;32m   1468\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Running serial generation for: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1469\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mpc_sub\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1470\u001b[0;31m                     \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpc_sub\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1471\u001b[0m                 \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1472\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/reV/reV/generation/generation.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(points_control, tech, res_file, output_request, scale_outputs, downscale)\u001b[0m\n\u001b[1;32m   1195\u001b[0m             out = Gen.OPTIONS[tech].reV_run(points_control, res_file,\n\u001b[1;32m   1196\u001b[0m                                             \u001b[0moutput_request\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_request\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1197\u001b[0;31m                                             downscale=downscale)\n\u001b[0m\u001b[1;32m   1198\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1199\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/reV/reV/SAM/generation.py\u001b[0m in \u001b[0;36mreV_run\u001b[0;34m(cls, points_control, res_file, output_request, downscale, drop_leap)\u001b[0m\n\u001b[1;32m    309\u001b[0m                                          \u001b[0mpoints_control\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_points\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtech\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                                          \u001b[0mdownscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownscale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m                                          output_request=output_request)\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;31m# run resource through curtailment filter if applicable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/reV/reV/SAM/SAM.py\u001b[0m in \u001b[0;36mget_sam_res\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    528\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_sam_res\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0;34m\"\"\"Get the SAM resource iterator object (single year, single file).\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSamResourceRetriever\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/reV/reV/SAM/SAM.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(cls, res_file, project_points, module, output_request, downscale)\u001b[0m\n\u001b[1;32m    240\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hsds'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhsds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreload_SAM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/renewable_resource.py\u001b[0m in \u001b[0;36mpreload_SAM\u001b[0;34m(cls, h5_file, sites, hub_heights, unscale, hsds, str_decode, group, require_wind_dir, precip_rate, icing, means)\u001b[0m\n\u001b[1;32m    969\u001b[0m                                        \u001b[0mrequire_wind_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequire_wind_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m                                        \u001b[0mprecip_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprecip_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0micing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0micing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m                                        means=means)\n\u001b[0m\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mSAM_res\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/renewable_resource.py\u001b[0m in \u001b[0;36mpreload_SAM\u001b[0;34m(cls, h5_file, sites, hub_heights, unscale, hsds, str_decode, group, require_wind_dir, precip_rate, icing, means)\u001b[0m\n\u001b[1;32m    969\u001b[0m                                        \u001b[0mrequire_wind_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequire_wind_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m                                        \u001b[0mprecip_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprecip_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0micing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0micing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m                                        means=means)\n\u001b[0m\u001b[1;32m    972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mSAM_res\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/renewable_resource.py\u001b[0m in \u001b[0;36m_preload_SAM\u001b[0;34m(self, sites, hub_heights, require_wind_dir, precip_rate, icing, means)\u001b[0m\n\u001b[1;32m    905\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mh_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mh_pos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msam_pos\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mheight_slices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                     \u001b[0mds_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{}_{}m'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 907\u001b[0;31m                     \u001b[0mSAM_res\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msam_pos\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mds_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh_pos\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mprecip_rate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/resource.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, keys)\u001b[0m\n\u001b[1;32m    467\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mResourceRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/renewable_resource.py\u001b[0m in \u001b[0;36m_get_ds\u001b[0;34m(self, ds_name, ds_slice)\u001b[0m\n\u001b[1;32m    786\u001b[0m         \u001b[0mvar_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    787\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mh\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mvar_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheights\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 788\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ds_height\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    789\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/renewable_resource.py\u001b[0m in \u001b[0;36m_get_ds_height\u001b[0;34m(self, ds_name, ds_slice)\u001b[0m\n\u001b[1;32m    741\u001b[0m                               ExtrapolationWarning)\n\u001b[1;32m    742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 743\u001b[0;31m             \u001b[0mts1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{}_{}m'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    744\u001b[0m             \u001b[0mts2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{}_{}m'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/resource.py\u001b[0m in \u001b[0;36m_get_ds\u001b[0;34m(self, ds_name, ds_slice)\u001b[0m\n\u001b[1;32m    915\u001b[0m         out = ResourceDataset.extract(ds, ds_slice, scale_attr=self.SCALE_ATTR,\n\u001b[1;32m    916\u001b[0m                                       \u001b[0madd_attr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mADD_ATTR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m                                       unscale=self._unscale)\n\u001b[0m\u001b[1;32m    918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/resource.py\u001b[0m in \u001b[0;36mextract\u001b[0;34m(cls, ds, ds_slice, scale_attr, add_attr, unscale)\u001b[0m\n\u001b[1;32m    218\u001b[0m                    unscale=unscale)\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mds_slice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/resource.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, ds_slice)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0mds_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extract_ds_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unscale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unscale_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/rex/rex/resource.py\u001b[0m in \u001b[0;36m_extract_ds_slice\u001b[0;34m(self, ds_slice)\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0midx_slice\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mslices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0midx_slice\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx_slice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/h5pyd/_hl/dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    786\u001b[0m                     \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"select\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getQueryParam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpage_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpage_stop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msel_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    787\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 788\u001b[0;31m                         \u001b[0mrsp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGET\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    789\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mIOError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mioe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"got IOError: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mioe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/h5pyd/_hl/base.py\u001b[0m in \u001b[0;36mGET\u001b[0;34m(self, req, params, use_cache, format)\u001b[0m\n\u001b[1;32m    892\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"object not initialized\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m         \u001b[0mrsp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_http_conn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGET\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_cache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrsp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Got response: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrsp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/h5pyd/_hl/httpconn.py\u001b[0m in \u001b[0;36mGET\u001b[0;34m(self, req, format, params, headers, use_cache)\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m             \u001b[0mrsp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_endpoint\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverifyCert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrsp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mce\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GET'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    531\u001b[0m         }\n\u001b[1;32m    532\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/requests/models.py\u001b[0m in \u001b[0;36mcontent\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    826\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCONTENT_CHUNK_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content_consumed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/requests/models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    748\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'stream'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    751\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mProtocolError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_fp_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 564\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    566\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m    505\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m                 \u001b[0mcache_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfp_closed\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m                 if (\n\u001b[1;32m    509\u001b[0m                     \u001b[0mamt\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    455\u001b[0m             \u001b[0;31m# Amount is given, implement using readinto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m             \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;31m# connection, and the user is reading more bytes than will be provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0;31m# Ideally, we would raise IncompleteRead if the content-length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/rev-hsds/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    590\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Run reV Gen in series locally\n",
    "ts = time.time()\n",
    "wind_gen = Gen.reV_run(tech='windpower', points=wind_points[::100], sam_files=config_path,\n",
    "                  res_file=wind_file, max_workers=1, fout=None,\n",
    "                  output_request=('cf_mean', 'cf_profile'))\n",
    "\n",
    "tt = time.time() - ts\n",
    "print('Time to compute generation for {} sites = {:.4f} min'\n",
    "      .format(len(wind_points), tt / 60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mrossol/miniconda3/envs/rev-hsds/lib/python3.7/site-packages/seaborn/utils.py:279: UserWarning: Use the colorbar set_ticks() method instead.\n",
      "  ax_i.set_yticks(newticks)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract CF means\n",
    "cf_means = wind_points_meta[['latitude', 'longitude']].copy()\n",
    "cf_means['cf_means'] = wind_gen.out['cf_mean'] / 1000\n",
    "\n",
    "cf_means.plot.scatter(x='longitude', y='latitude', c='cf_means', cmap='Purples')\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zQQPLJCIiEdfIGtM9wBnGmFOA/cCbgLfUHrTWjgHdtdvGmDuBv1CvPBGR5a1hNSZrbRV4D3ArsBO40Vq7wxhzrTHmykYdV0REFrdYEATH3ipCjDH3Wmvn09y3uF6YiEg0zNY/4ITSzA8iIhIpCiYREYkUBZOIiESKgklERCJFwSQiIpGiYBIRkUhRMImISKQomEREJFIUTCIiEikKJhERiRQFk4iIRIqCSURkCcmXq4wVKie7GM+KgmkWA+NFBsaLeL7mgRWRxaNQ9rh/zygD48WTXZRnRcE0i6GpEg/sG+WuJ4foHclT8fyTXSQRkTkVKx537x7ixnv3MThZOtnFeVYUTEfRlk2RSyXYNTDJr54YYs/gFMWKd7KLJSLyNKWqx327h/nHWy3fe7CP4anyyS7Ss6JgmuHnjw9y9Rfv5Zv391KseKxoztCWTbFnOM9dTw7x+MEJ8uXqyS6miAgAFc/ngb0jfPzHj/HQ/nHe9bJT2bK69WQX61lp5NLqi9KZa1vp6crxjft6+d72Pi47ew1Xnbee7pYMfhBwcLxI70iB1W1ZNnTlaM2mTnaRRWSZqno+23vH+Phtj3P/3lHefdFpvOyM7pNdrGdNwTRDd0uG/+fKs3mod5wfPNzPzQ/2ccv2fi42K/nt5/awoauJIAgYzZc5OF6kqyXNpq4m2nMpYrGjL/zo+QFV38f3wQsCPN/9xICWbJJUQpVXEZk/zw/Y0T/GJ26z3L17mHe8ZDOvfs5aBqeKi/77REurz2Jn/xhj+SrNmSQHx4t854H93PbIQcpVnxee2sXvnN/DmWvaAJgqVSlUqrRmU3Q1p6n6AeWqT8XzqVR9Kn5wROeJWAyCwK1dHHD4sj2XYnVrlramFM3pxJwhd7w8P6BY8UjEY2SS8QXdt4iceL4fsLN/jH+47THutIf4vRds5I3P38jwVImuljRnrWkj+czD6aR/QSiYZrFrYIK9w3laMymyqQQAY4UK39vex/e39zNZqnLOujZe97wNnL+xg1gsRrHiUar6JOIx4jGIx2LhdXd7rjAIgoBS1Z8+d5WIx1nVlmFFc5rWbIp0cv6/YFXPp1j1KZQ9xgplRvMVpkoeAa52Fo/FaM4mac0kac0myaWSZFJxBZbIIhEEAY8eGOcTtz3Oj3ce5PXP6+GtL9zESL68EKEECqbjdyKCyfMDhiZL7BmaYqpcJZtM0pxxrZ6Fssetjxzguw/sZ3CyzCndzfzO+T289PRuEvHYEfsoVDwKZY98uRpeeuQr7na+7FGseKxpy3L2unZWtmaOeG6+XKUc1rRasylWt2Voz6VoySSnA6Tq+dPHGC1UGMuXyVc8ah9pKh4nk4qTThwOHT+oq9FN1+RiEAtoSadozSVpyybJphJkkgkyyTjx+En/PRURXCg9MTDJJ29/jFu2H+DKbet450tPWchQAgXT8TsRwTS9gyBgrFBhz3Ce4ckyqUSc1mySeCxGxfP5/x47xDfv76V3pEBXc5pcKuECqFKlWDm+sU+rWjNsXdfG2WvbOXt9Gz0duekwKVY8ChUP3w9IJmJ0NKXDJsTD3dfTiTiZZGK6dlWseOwbzrNnKM/uoSkmilXWdmRZ35FjfUeOdR256dpg7bWWPZ9KNaDseUe8gS2ZJC2ZpOtCn06EoaXAEjnRnhqc5FO37+Lb/7Wf3zp7DX968WkLHUqgYDp+JzKY6k2WquwfydM3ViQOtOfSJOIx/CDg7qeGudMOEIvFaEonwp8kufrrqcT0Y7nwvkwyzt7hPDv6xtjRN84jfeOMhlOJtOdSbF3bFoZVG6eubCERj+H5AaWqRyoRJ5WI4/kB/WMF9gzl2TM0xe7wsn+sOP0GpBNxWrLJp41t6G5JT4dUT2d42dHEytbMdO0vCAIqngutctUHAneOLBYjl4rTlkvRmk2615Ny4ZhQYIksuL1DU1x/xy6+fm8vl5iVvO/SLYwufCiBgun4naxgqilWPAbGi+wdzlPxAtqO8xzQXIIgoG+0yI7+w0F1IJxaJJdKYNa0cs66NtLJ+HQA7RsuTDf5xWOwtj3HphVNbOpqYtOKZjavaGZNe5ZE3J0H6x8rsH+0yP6RPPtHC+5npMBU+XDtKxmPsbYjR08YWBu7mtjQ1cT6ulpWEARUfXdurFL18cNzWAHQkUuxcUUznU1z91QUkfnZP1LgX+7cxX/8ei8vOb2bv3ylYazQkFACBdPxO9nBVFP1fAYnSuwezlOoeDSlXC3ID1yNonYZMON27TruMh6LkQ1rGrMZmizxSP84O/rG2dE3xp6hPAHQ2ZQKg+dwAG3oyh11P3OpNVnuHy3QVwur0QK9IwX6xw7PGRiD6fFbG7ua2NDpAmtDZxO59OHj1s6hNaUTnNLdzIqWjGpRIs/QwbEin/3PJ/jCL3Zz4eYu/vpVZzJRqtDV3JBQAgXT8YtKMNX4fsBoocLuoSnGCxXiMderLhGPkYzHiMdjJGLueiL8icchGW5T9QIGJ0tMlsIeebEY2VTiiPM/9SZLVTw/oD137IG9Fc93NRrPJwjDME6MVDJOKhEjlYgTP0aNpuL59I8V2TecZ+9wnn0jefYN5+kdKVCtm+R2ZWuGDZ1NbOxq4jfO6GbL6lZKVY/JUpVkPMamFc2sass8o+AUWY6CIGBgvMgXfrGbf/vZk5zb08H/fPVWJssNDSVQMB2/qAXTEQcMgmfcdFXxfKZKVcbyFQYny4yXKsSpnctJzNmdu3YeqFT1ppv1ALKpBO25FO3ZFE2ZBH4AhXKVyZLHZLHKVLlC/QTqiZgLq3QyTjIeO+aA4QNjRfaGQbVvOM/ekTy9YdPieRs6eN3zejh3fTueHzBWrBAD1ne6JsH6GpaIHKlY8XhiYJIfPdzP9Xc+wVlr27jmirPJV6p0NqXZurZhoQQKpuMX5WBaSLWgGi9UODRZZqLoOkXEY26QbMVzM0nURum2ZJK051K0ZZPkMq6zxbFGf9d64pWrrmZVKHtMlCrkSx5T5SoEHNGBIp08suv5bPLlKj96+ADffaCP4XyZLatbeN3zNvCCU7oIApgoVfD8gFWtWXq6crRpSieRaUEQcGiixM4D49zx6ABfuWsvp61s4dqrzqZY9ejIpTl7XUNDCRRMx2+5BNNMFc8nX3KDZseKVZrTCdpybgBwLrXwPeFqg35LFZ9ipcp4scp4wdWygqDWzQHSCddFPZU4soZVrvrc/uhBvnX/fg6MF9nQ1cTrzl/Py85YSSIeY7JUpVT1aG9Ks3lFMx25lLqfy7JWrHg8eWiS7b1jfOWuPTzcN855Gzr4q986k7J/wkIJFEzHb7kGU1TUAqsYDuydCGt1UyWP2lueiB8e7+X5AT/fNchN9+1j91CeVa0Zfvu567l062oyyYTrKFHxSCfidNS6nmeS02OlGjXnVxAE+AHT597qO6cc0WEFCHyIxSEzjxqjyPGqryXdvnOAr9+zj3gsxtUvPYVXbl3NSKF8IkMJFEzHT8EUTb5/OLAGJ0vsHy0Q4/B4ryAIuHfPCN+4r5ed/eN05FJcuW0dlz9nLc2ZJFXPn25W9Op+J1OJcKxUxs2+UevBOLOLfu08m+cHVHyfqhe46ZnCqaKKVZ9SeL3q+dO/HIfrfhCru1X/eP3teAyawxkymtMJcuF4tHQDQ1SWrlLVnUvavm+UL921l0f6xzl/Ywd/esnpdLdkGM6X6GrKnMhQAgXT8VMwLQ7FisfBsHOE57vxXrUv7h19Y9x4by/37x2hKZ3g8nPWctGWlaxpzz6tN2JtQPH0ObVQMh6jJZvE86BY9aanV5r+HxVOlut6RMaP6An5bJo9/SAIJ+h1l37tRFzMhWhzJkFLxs1FmE25QdYLNc5NlpZDE0Ue6R/nxzsOcuN9vaTirpZ06VmrKVQ8pkpVNnc3s2lF84kebqFgOl4KpsWl4vkcGi+xe3iKUtWjJX14YtwnDk1y0329/GLX4PSHtaI5zbqOHGvbs6xtd5frOrKsaTuyJ58Xztoeix0OnJogcPMUjuYrjOTLR1yOFyuuNuX74T6CcEmSw/dVp+/zqfoBvh/Q2ZxmTVuWNe1ZVrdl3fW2LB11g4hrZSpXfSq+TwzwcYOju5rTdDal3cwfqYTOpy1jtVrSg/tG+dKv9rDzwAQXbOrkTy85na7mNCP5Mk3pBGZN27yGhTTASf/lVDDJCTFzYtxcyk1jBHBgrMhjByfoHyvQN1akf6xI/1iB0XzliH10NqWmw2ptR46WdIKRQoXRfIXRGQFU322+Jh6D5kySdOLwOLNkIj49xuyI6/EYyUSMRDxOPAZDk279raEZ0zqlk/EwqDLTgVW7XN+ZI5WIUwmbFA/P0BGjPZekqznjZnhPJzS+a5k4NFFkZ984P9pxgJvu208qGeMPf+NULjGrKIQTPG/ubmZDZ9OJbLqbaWkHkzHmMuA6IAF8zlr7sRmPvx94J1AFDgHvsNbuOcY+FUyLmFtkscKeoSmG82WyycQRM6bXy5erHAiDqm/MzULRP+ouawHhzmOl6GhK0dGUdpe5NJ11tzvDy7Zs6lk3iZSrPgcnihwcK3JwvMiB8OfgeIkDY8WnTax7xuqW6TkPz1zTRkvGzQ5SqvgUq950p4tcKkFXiytney6loFpiCmWPpwYneWDfKDf8cg/24AQXbu7iTy4+jY6mNKOFMs3pBGZtWxSGUCzdYDLGJIDHgFcAvcA9wJuttY/UbXMJ8Gtrbd4Y88fAxdbaNx5jvwqmJWK8WKF3uMDB8SLJeCwc2Pv0ruezqc24vhBhs1CCIGC8WOXguAvTXQMTPNI/zhOHpqZXK960oomt69pdWK1tm17upOKFXfO9KjFirGrNsLY9R7u60S9qFc+ndyTPU4emuO2Rg3zz/l4yyQR/9LJTuWjLyrCW5HFqdzM9XU1R+V0+6YVo5NLqFwK7rLVPAhhjvgZcBUwHk7X2jrrt7wJ+r4HlkYhpy6bYui7F5u4mBidKTJar5Etuban6GSliuKY1N6O6a3Kba9omP3Dnhfywy7e77Za0r5/D8PD+nSN74sXc4oox11Ovtupwbbt4LEYqHieZONwkGIvF3EwbuRRbVrdy0ZaVgAvRxw5OuIl5+93AyR881A+Ey52ENaqta9vY0NVEDBjLVxmYGCUZj7OhM8fKtsx006dEn++76YR2HXK1pJvu62X3UJ4XntrFn1x0Om25FENTZVozSS7Y3Enrya8lRUojf9PXA/vqbvcCL5hj+6uBHzawPBJRTekkG1cc/lWcudRGOZxzb6rk2uDLRTevYC1cZuvSXVsWJJWMkYonSIXTLNWmXIqFqwzXrzBcfxmPxY7Ypr5MFc/NklE7J1Aoe5TCKZfqwy0Zj0+vX3VuTwfn9nQA7nzbU4NTPNLvgurB3lHufOwQ4M6jXbRlJb955ipO6W6h6vnsHc7z1NAUrdkUGzpzdDan1TU9wkamyuwamOCxg5N88/5e7t87yuq2DH992Zm8+LQVFCoeI/ly1GpJkdLIYJrt3Z61ec0Y83vABcBFDSyPLBKxWIx00jXtkXn6474fhpbner7VlrBPhBPmNqbpK0YyATmOUkvzD4dWxQumw3Rossx4OJ1UIhab7uhw+qoWTl/VwpXb1hEEAQfHS+zoG+Oup4a4ZXs/33mgj80rmrjErOKiLStZ0ZKhUPZ4pH+cWIywh2COtuzs5+fkxJsqVXni0CRPHJri+9v7+OmjA+RSCd7+4s1csW0d8ViM4XyZFtWSjqmRwdQLbKi73QP0zdzIGHMp8DfARdbaUgPLI0tEPB4jGz96U97JcLQynb7KdQ9200lVGJwsMTTlfs3j1IIqzpp21xX95WetZrxQ4We7Brnj0QH+/Ze7+eIvd7NtQweXmJW86NRuMqk4gxNl+kaL5FIJ1nfm6Gp2XdEVUideqeqxdyjPE4cmuX3nALds76fs+bz6OWt54/M30pZNMlGsUvZ8TlvZzPpO1ZKOpZGdH5K4zg8vB/bjOj+8xVq7o26b5wI3AZdZax+f537V+UEWtdq8h+PFCkNTJcbyVXc+C6bnPqwFTN9ogZ/aAe60AxwcL5FJxnnRaSu4xKxiW08HfhAwUawQBJBKxlnVmmFFS4aWTFIDexustnr0EwOT/HLXEDf9Vy9Dk6XhXVcAABEqSURBVGVedOoK3vaizazvzJEvV5kqV1nTlmVzd/NiOU940lOz0d3FLwc+iesu/gVr7UeMMdcC91prbzbG/AR4DtAfPmWvtfbKY+xTwSRLiucHTJWrTBaqDE6VGJ4qE8ONuap1Gw+CwHWcsIf4+a5DTJU8uprSvGzLSi7aspJTVzbj+wH5skfVd1MuteVSrG7N0JZL0ZxOqnffAqhNvTVZqrBrYJL/2us6Njw5OMXpq1q4+iWncM76diqez1ihQmsmyRmrW2lvWlTNdif9F0UDbEUipljxGJ4s0zuSZ6pcJRmP01rXLb5c9bln9zB32AHu3TOC5we0ZpM8Z3379M+Gzhxlz82A4QcByXiM7pYM3a2uNhWlZtCoqZ0vLFV8Sp5rhp0oVZkqVSlVfCBg/2iBb92/n3v3jNDdkuFtL9rEy7asJAhgrFAmmYhzxqoWulsyi/EPgpNeYAWTSEQFQcBkqcrAeIn+sQIVLyCXShxxLmmsUOHe3cNs3z/GQ/vHODThzl915FKcUwuqnnbWtmUphl+0QQCt2SRr2rOsbF3eqwoXK65JdbbwIfy3WPE5NFFkYKLEwfESe4enuPupYTLJBK+/oIcrt60jnYi76a78gFNWNLMunPVjkVIwHS8FkyxHnh8wVqjQN1pgcLL0tKY+cEF2cKLEQ72jLqh6x6ZnyOhsSoW1qQ7O7WmnqznFVNnNUlGb4ikCMw6cMBNF9172jRXdnIY+DE2VGBgvcXC8yP7RwvTPRDg8AdxQhDVtWc7f2Mkbn7+BjqY0UyW3dMu6jiybVzQvhdqogul4KZhkuas19e0fzTNZck19zZnk0/5CD4KA/rEiD4W1qYd6xxjOu6Dqbknz8jNXc9k5a0gn4pQ9j9Zcio2dTaxoySzJXmO+HzBaqLB7aIqRqTKP9k/wn48f4snBKQYnSkd8YXQ1p1nfkWNdR471HVnWd+RY39HE6rbM9Bx2parHRLFKe1OK01e1LKVgP+kfvoJJZJGqb+obnCyRr3jEcTNlZFMJMqk48bru40Hgzo08tH+Mu58a5r49IyTiMX7jjG6u3Laens5ceE4rxobOJlbPsgzJYlTxfAYnSuwemmKsWOHep0b40Y4D9I4U6GpKc25PexhALojWdWRn7T1XP3t82fPJpuKcHp5HWmLd9E/6i1EwiSwR5apPvlxlolhleKrEWKE6vUJvJuGCqr5W1Tda4Jbtffxk5wCFisfZ69q4ats6zt/YSb7i4fkBq9oy9HQ00ZZbfAN5C2WPvrA5bmSqzM8eH+TWRw4wUaxy2spmrjpvPS89vfuI96QWPhXPLXlS+370cZPy1tbbasum6GpOn8wZwBvppH/QCiaRJcr3Xa+8qXKV0XyFockSpapbeiMRi9GUdmOdJktVfvLIQb63vY+BiRKr2zJcce46Lj1rFUEQo+S5ZUo2dTXR1ZKJ9PioIAgYL1TpHc0zMF6idyTP7TsH+PmuQTw/4AWndnHVtvWcva6NWCzGZKlKqepNTydVW4CyOZ2kNZucXi05k4wv1RCajYLpeCmYRJ652iwUE8UKfWNFCmWPXCpBcyaJ5wf8+qkhvvtAH4/0j5NLJXjF1tVcce46OptTTJZcJ4BMMk57U4r2bIqmdDKcUDe+IDWq6YUWPR8/YLrGNz3xbrhgcBBOzOvjJuwF8AMYnCgxVqzwSN84tz1ygIf2u9dx6VmruGLbOta256Znga94Pl0taXo6cmRTLoAWcU+6haRgOl4KJpGF4Yc9/faO5BmZKpOIxabHSz1+cIKbt/fxs8cH8cOaxpXb1nPOujaqfhBOrusfnkg35moabdkkbTm3SnE2eeSy8rWJcGtNZa7p0SNfcbPK58tVvABqzwhXrKc20/vhHbnjxdxDxMLjFysev3xikB8+fID+sSIrWzNcce5aXrF1DS1h8E6EXbrXdmTp6WyiJbMoZmI40RRMx0vBJLLwpkpVt4LwaAE/cEuSpBJxhiZL/ODhA/zw4X4milVyqQTrOrKs72iip/Nwh4G17VkS8dj0svLg/gNmk3Ga0kk3DVO5ij9j+ZBkPI7n+4zmKwznywxNljk0WWJ0qkwlXOa+6vtHXLr73Tmg2mXFc6FTqvqY1a1cdd46XnxaN4l4jIrnM1GsEIvF2NCVY217bkl06mggBdPxUjCJNE656nqw7RmeolT1wwG9SUpVj1/sGmTXwCT7Rwv0jhQ4NKOL9Yqwi/X6MLDWd+ZY3ZolHoPhqQrD+ZILnokShyZL7nKixESpekQZ4jE3nVI6XOo+mXDrXs1c/yoZP/J6Lp3goi0rOXNNG+CaLSdLVVKJOJtWNLGqNRvp82MRomA6XgomkcarjfnZOzTFSL5CKhGnNZs8ovt5uerTP+ZCav9ogf3hZe9onqmSd9R9N2cSrGzJsLI1w8rW7PT17pY0K1szrGh+duOo8uUq+bJHUzrBKd3NS3ZcVgOd9DdLDawi8jTxeIyu5jRdzWkmS1X2j+TpHysSEJCMxadrKT2dTWxa0XzEc2udC3pH8uwfLeD5gQuhMIDmmmHbDwI8P6BcduevgrCzA2Hnh+nbHO4YMd0hAte9u6MpzZbVrXQ0pRZdF3dxFEwiMqeWTBKzpo3N3c2MF6oUK2713qmSW9Kh4vnE6jooxHBNbKd0N7NldSuJeAzfD6j6bmn78UIFzw/ww6U+akvV+0FAPAbZZIJMKkEyHiMWd+tWxeO1FYYhTpx4nOnVh+Ph4pAxIJtOLKUZGJYtBZOIzEsmmWBl69M7DUzPiOD5VMLeelPlKoWKT6FUZbLkkUrEyaQSNIe99bLJBOlU/IhzRKlEXE1uAiiYRORZSsRjJCK2orAsbuqiIiIikaJgEhGRSFEwiYhIpCiYREQkUhRMIiISKQomERGJFAWTiIhEioJJREQiRcEkIiKRomASEZFIUTCJiEikKJhERCRSFEwiIhIpCiYREYkUBZOIiESKgklERCJFwSQiIpGiYBIRkUhRMImISKQomEREJFKSjdy5MeYy4DogAXzOWvuxGY9ngC8BzwOGgDdaa3c3skwiIhJtDasxGWMSwPXAq4CtwJuNMVtnbHY1MGKtPR34J+DvGlUeERFZHBrZlHchsMta+6S1tgx8DbhqxjZXATeE128CXm6MiTWwTCIiEnGNbMpbD+yru90LvOBo21hrq8aYMWAFMDjHfud6rJ4CTkRkEWpkjWm2YAiewTZHsNZe9oxLJCIikdfIYOoFNtTd7gH6jraNMSYJtAPDDSyTiIhEXCOb8u4BzjDGnALsB94EvGXGNjcDbwN+BbwO+Km1ds4ak4iILG0NqzFZa6vAe4BbgZ3AjdbaHcaYa40xV4abfR5YYYzZBbwf+OtGlUdERBaHWBCogiIiItGhmR9ERCRSFEwiIhIpCiYREYkUBZOIiERKQydxXayMMT8Cuk92OURETqDBqExgoF55IiISKWrKExGRSFEwiYhIpCiYREQkUhRMIiISKQomERGJFAWTiIhEyqIYx2SM2QB8CVgD+MC/WmuvM8Z0AV8HNgO7gTdYa0eMMWcC/w6cD/yNtfYfw/1kgf8EMrjXfpO19sNHOebbgA+FN/9fa+0N4f0fAX4f6LTWthytfMDPgS+HZSvjlgF5A3Ap8FHgdKACfHA5l2+Oz/YS3LIoGeAAsC38bP8c+N9ADrjOWvu+5freRb18+myXdvmO8vznAV/EfYY/AP7cWhsYY/4BuCIs8xPA2621o0fbz2KpMVWBD1hrzwJeCPypMWYrbpmM2621ZwC3c3jZjGHgvcA/zthPCfhNa+024DzgMmPMC2ceLAy8D+OWgr8Q+LAxpjN8+HvhfXOWDxeM9wPX4tab2heW72HcB3w/7hdsuZfvaJ/t/w2PnwEKwGfD7e8F3grsPQFlm7V8ROe9i3r59Nku7fLN5tPAHwJnhD+1Abs/Bs6x1p4LPAb8j7l2siiCyVrbb629P7w+gVvfaT1wFXBDuNkNwGvDbQastffg/nKo309grZ0Mb6bCn9lGGP8W8GNr7bC1dgT3pl4W7uMua23/Mcr3JNABPD8s15fCY73WWrvTWvsrYALwlnv5jvLZPgfoAv5XuHDkJ4FXhtv8wlr7zZnHXY7vXdTLp892aZdvJmPMWqDNWvur8LP9Eoe/k2+zbo0+gLtwK5of1aIIpnrGmM3Ac4FfA6trb1Z4uWoez08YYx4ABnAfwq9n2Ww97i+Rmt7wvvmW7zxcdbVWvl6gU+WbV9mei2veidf9R3gYaDqZZasrXyTfu6iXT5/t0i5f3fN75/H8dwA/nGtHiyqYjDEtwDeB91lrx5/JPqy1nrX2PFxiX2iMOWeWzWKz3HfMuZvqyvcJZvzVMp/nL+fy1X+2QH4+ZTlRZZtRvsi9d1Evnz7bpV2+43m+MeZvcE2U/zHXjhZF5wcAY0wK9+H8h7X2W+HdB40xa621/WE1cmC++7PWjhpj7sS1tzZzuJ37b3FJf3Hd5j3AnXOULQHcB5yKq6Z+GfdXwcGwXD3AqMp31PJlgD5cM8R5uLZzv/bZAudwHF9oy+y9i3r59Nku/fKB68zyaY5souvBffa17d8GvAZ4edjUd1SLIpiMMTHg88BOa+0n6h66GXgb8LHw8rvH2M9KoBJ+ODlcT5a/C6u159Vt1wV8tO7E3yuZ+2SdD2wH7rSHexJNADYs10W4JgyV7+n7jAH/BnyhVrbw/mHgQ8aY9+D+0v7JiS5bKLLvXdTLp892aZfPWuvVP79WPuM6Vvwa1xnjn8P7LwP+CrjIWnvMP0QWxezixpiXAj8DHsJ9WAAfxL34G4GNuJ48r7fWDhtj1uB6+LSF208CW3FdLG8AErhmzButtdce5ZjvCI8B8BFr7b+H9/898BZgHe6vgc/h/mPNLN8XgD8GNuG6SN4HvB73y3Q9sBZXzfWAg8u4fLcBb+fpn20J9x8uHR5/W/jZvh33ZZcIy1fGtbEvx/cu6uXTZ7uEy2etvWaW51/A4e7iPwT+zLru4rtwvTCHwk3vsta+e7YywCIJJhERWT4WVecHERFZ+hRMIiISKQomERGJFAWTiIhEioJJREQiZVGMYxI50YwxHq6bbgo3Uv0G4JPWWn+O52wGXmyt/b9zbPMcDk+yuREYC38GceM+PmWtfd1CvAaRxUrBJDK7QjiFC8aYVbgZsdtxsy8fzWbcWI+jBpO19iHCQYnGmC8Ct1hrb6rbRKEky56CSeQYrLUDxpg/BO4xxlyDG9z4ZaA53OQ91tpf4mYgOcu4yTJvAD4V3ncxbnDh9dbaz3IUYY3rFmvtOcaYP8DNzJzATdvzcdyA1LfiBqheHg5KPQ03sHIlbmqfd1lrH124Vy9y4ukck8g8WGufxP1/WYWbm+wV1trzgTfiAgjcujg/s9aeZ639J+BqYMxa+3zcUgXvMsacchyHPQdXA7sQ+AiQt9Y+Fzff3O+H2/wrbnT984C/AP7lWbxMkUhQjUlk/mqzJ6eA/2OMOQ839cuWo2z/SuBcY0ytea4dt3jaU/M83h3WrcMzYYwZwy3WBu7c17nGzTr9YuAbxpjaczLzfTEiUaVgEpkHY8ypuBAawJ1nOghsw9Wiikd5WgxXm7n1GR62VHfdr7vt4/7vxoHR2rkwkaVCTXkixxDOzvwZ4P+E0/W3A/1hD7234s4DgVvaobXuqbcCf2zcki0YY7aESw0siHBNsqeMMa8P9x8zxmxbqP2LnCyqMYnMLhd2Yqh1F/8ybqE2cOdxvhkGwh3AVHj/dqBqjHkQN8PydbieeveHS0AcIlxqegH9LvBpY8yHwrJ+DXhwgY8hckJpdnEREYkUNeWJiEikKJhERCRSFEwiIhIpCiYREYkUBZOIiESKgklERCJFwSQiIpHy/wO53GZ50bT2oQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract CF profiles\n",
    "cf_profiles = pd.DataFrame(wind_gen.out['cf_profile'] / 1000, columns=points)\n",
    "cf_profiles['DateTime'] = wind_ti\n",
    "cf_profiles['Month'] = wind_ti.month\n",
    "cf_profiles['Hour'] = wind_ti.hour\n",
    "\n",
    "sns_profiles = pd.melt(cf_profiles, id_vars=['DateTime', 'Month', 'Hour'],\n",
    "                       value_name='CF')\n",
    "sns_profiles.index = pd.to_datetime(sns_profiles['DateTime'])\n",
    "\n",
    "mask = sns_profiles.index < pd.to_datetime('2013-01-02')\n",
    "ax = sns.lineplot(x=\"DateTime\", y=\"CF\",\n",
    "             markers=True, dashes=False, data=sns_profiles.loc[mask])\n",
    "plt.title('CF Profile for 2013-01-01')\n",
    "ax.set(ylim=(0, 1))\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "ax = sns.boxplot(x=\"Month\", y=\"CF\", data=sns_profiles)\n",
    "plt.title('Diurnal Distribution')\n",
    "\n",
    "ax.set(ylim=(0, 1))\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "ax = sns.boxplot(x=\"Hour\", y=\"CF\", data=sns_profiles)\n",
    "plt.title('Diurnal Distribution')\n",
    "ax.set(ylim=(0, 1))\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
